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  • Matthew Haller

Is Your Sales Compensation Program Working?

Leveraging Sales Compensation Analytics to Refine Your Sales Incentives


The sales compensation program is one of the most important components of any company’s go-to-market strategy. It is the single most powerful method of communicating the corporate plan to your sales reps because it operationalizes the sales strategy and puts financial incentives behind strong performance. Many companies fail to fully realize the benefit of incentive compensation dollars because they do not look under the hood. Consequently, they fail to learn where the compensation program is successful and where they are wasting money. Over the next few pages, we’ll explore this powerful tool - leveraging data to drive sales.


Why Are Analytics Important for Sales Compensation?


Today, executives and business leaders have more access to data than ever before. This data gives visibility into critical functions of the business. It allows them to fine tune their strategies and find alignment and correlations that would have previously been unidentifiable, offering new opportunities to drive cost-effective growth.

Sales compensation programs have benefited from this increase in technology and data as well by giving executives insight into sales rep performances to:


  • Better Manage Costs

  • Assess Rep Performance

  • Benchmark Sales Team Health

  • Refine Planning Processes

By running the proper analytics, you will be able to tie sales and incentives together and gain precision in your go-to-market investments.


Analytic 1: Rep Performance Histogram

The histogram is one of the first analytics that teams should run when assessing both team and rep performance because it answers the question “How many of my reps hit quota this year?” It is calculated by counting the number of reps of the same role/compensation plan that fall within equal performance buckets. The chart below shows a completed histogram, which tells us that 75% of this figurative sales team achieved quota last year.


This sales team’s distribution looks healthy because there is a clustering around 100% to goal in a normal bell curve shape. Sometimes when the bell curve is shifted to the right of the chart - as is the case in the example above - it can lead to increased costs of sales because the company will pay out more accelerated incentive dollars. In other cases, it is shifted to the left, where reps feel as though quota is difficult to achieve, leading to increased turnover and higher recruitment costs.

Sometimes the histogram isn’t shaped like a bell curve (below), and the shape of the graph is inverted. On the right hand side, there are a number of “winners” who blew out their quotas to earn large payouts while the “losers” struggled to even achieve 80% to goal.



This “inverted bell curve” can be an ominous sign for any sales compensation program because it often means that sales performance is driven by reps that were able to overachieve - meaning the company was paying accelerated commission rates to hit the company target, leading to increased commissions costs.

In addition to increasing the compensation cost of sales (CCoS), this inverted bell curve also is a precursor to increased recruiting costs as low performers - who feel as though they cannot earn in their roles - leave to find new employment opportunities.

Analytic 2: Pay Curve Analysis


The pay curve analysis helps practitioners understand two key components of the compensation program: where to set the excellence point and how accurately the compensation program is administered. To develop this view, simply calculate the pay and corresponding performance level for each measure and plot them on two axes.

You’ll notice right away that the resulting graph should resemble the pay curve for the measure itself. Feel free to add the actual pay curve in the analysis (example below) to see how accurately the compensation program is being administered. The closer each data point is to the pay curve, the more accurate the administration.



Another benefit of the pay curve analysis is that it helps planners set the excellence points, or the % achievement in the compensation plan where the incumbent earns the full upside in the plan. These incentive dollars are typically reserved for the top 10% of performers, meaning that the more spread out the incumbents are on the pay curve, the higher the excellence point will be.


Companies that are able to effectively forecast the excellence point in their compensation plans are better able to manage costs because they minimize the number of reps who are paid high acceleration rates. This is not to say that companies are planning against reps to save money, but rather that the majority of reps will not achieve the full upside, as it is reserved for those who truly over-perform to plan (and deserve that full bonus amount!).


Analytic 3: Quota Size Correlation

The final analytic we will cover is the quota size correlation - an important metric for both managing sales compensation costs and refining sales planning capabilities. It is calculated by plotting the quota size for like roles against the achievement (not TI) in a scatterplot (see below). This view helps determine quota setting accuracy because it shows where high reps were able to blow out the plan, therefore increasing costs due to paying plan acceleration.

In most companies, it is normal for the performance spread to be wider for reps with smaller quotas and more transactional sales as performance will naturally vary. As quota sizes get larger and we move up to enterprise/global accounts, expect that the spread should be significantly smaller as the quota size makes it very difficult for a rep to blow out their number.

Another benefit of the Quota Size Correlation is that it can help companies manage the cost of their ramping program when they plot ramping rep performance alongside their tenured reps. Comparing both populations allows teams to track the progression of the ramping reps and leverage that data to refine ramping quota calculations. For example, by tracking 5 reps throughout the ramping process, you might find that they are able to hit 20% of a fully ramped quota in their first month, 40% in the second month, and so on. With these data points, it becomes easier to deploy ramping quotas because planners will be able to plan based on the full quota and apply the appropriate quota reduction to align with the rep’s tenure.

Although not technically a sales compensation analytic, the quota size correlation is a critical analysis that companies should continually monitor because of the visibility it provides into critical moments in the rep’s career and can significantly reduce costs.



Are you wondering whether your compensation program is fully aligned with your business? Set up a free consultation and we'll help you look under the hood!


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